TELKOMNIKA Telecommunication, Computing, Electronics and Control
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation
Subject
Fully convolution network, Image segmentation, Transfer learning, U-Net, VGG16
Description
A brain tumor is one of a deadly disease that needs high accuracy in its
medical surgery. Brain tumor detection can be done through magnetic
resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
medical surgery. Brain tumor detection can be done through magnetic
resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
Creator
Anindya Apriliyanti Pravitasari, Nur Iriawan, Mawanda Almuhayar, Taufik Azmi, Irhamah, Kartika Fithriasari, Santi Wulan Purnami, Widiana Ferriastuti
Source
DOI: 10.12928/TELKOMNIKA.v18i3.14753
Publisher
Universitas Ahmad Dahlan
Date
June 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Anindya Apriliyanti Pravitasari, Nur Iriawan, Mawanda Almuhayar, Taufik Azmi, Irhamah, Kartika Fithriasari, Santi Wulan Purnami, Widiana Ferriastuti, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3831.
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3831.